TY - JOUR
T1 - Combined burden and functional impact tests for cancer driver discovery using DriverPower
AU - PCAWG Drivers and Functional Interpretation Working Group
AU - PCAWG Consortium
AU - Shuai, Shimin
AU - Abascal, Federico
AU - Amin, Samirkumar B.
AU - Bader, Gary D.
AU - Bandopadhayay, Pratiti
AU - Barenboim, Jonathan
AU - Beroukhim, Rameen
AU - Bertl, Johanna
AU - Boroevich, Keith A.
AU - Brunak, Søren
AU - Campbell, Peter J.
AU - Carlevaro-Fita, Joana
AU - Chakravarty, Dimple
AU - Chan, Calvin Wing Yiu
AU - Chen, Ken
AU - Choi, Jung Kyoon
AU - Deu-Pons, Jordi
AU - Dhingra, Priyanka
AU - Diamanti, Klev
AU - Feuerbach, Lars
AU - Fink, J. Lynn
AU - Fonseca, Nuno A.
AU - Frigola, Joan
AU - Gambacorti-Passerini, Carlo
AU - Garsed, Dale W.
AU - Gerstein, Mark
AU - Getz, Gad
AU - Guo, Qianyun
AU - Gut, Ivo G.
AU - Haan, David
AU - Hamilton, Mark P.
AU - Haradhvala, Nicholas J.
AU - Harmanci, Arif O.
AU - Helmy, Mohamed
AU - Herrmann, Carl
AU - Hess, Julian M.
AU - Hobolth, Asger
AU - Hodzic, Ermin
AU - Hong, Chen
AU - Hornshøj, Henrik
AU - Isaev, Keren
AU - Izarzugaza, Jose M.G.
AU - Johnson, Rory
AU - Johnson, Todd A.
AU - Juul, Malene
AU - Juul, Randi Istrup
AU - Kahles, Andre
AU - Kahraman, Abdullah
AU - Brāzma, Alvis
AU - Vīksna, Juris
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
AB - The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.
UR - https://www.scopus.com/pages/publications/85079072523
U2 - 10.1038/s41467-019-13929-1
DO - 10.1038/s41467-019-13929-1
M3 - Article
C2 - 32024818
SN - 2041-1723
VL - 11
SP - 1
EP - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 734
ER -
SDG 3 Good Health and Well-being